Historical data replay utilizing a computer system

ABSTRACT

Described are methods, systems and computer readable media for simulated replay of data using a computer system.

This application is a continuation of U.S. application Ser. No.15/155,010, entitled “Historical Data Replay Utilizing a ComputerSystem”, and filed on May 14, 2016, which claims the benefit of U.S.Provisional Application No. 62/161,813, entitled “Computer Data System”and filed on May 14, 2015, which is incorporated herein by reference inits entirety.

Embodiments relate generally to computer data systems, and moreparticularly, to methods, systems and computer readable media forreplaying a time-period of historical data utilizing a computer system.

Historically, entities with large data production systems havemaintained a test environment for running simulated real-time data ortest data that is separate from a production environment for runningproduction real-time data. Maintaining separate environments can preventthe contamination of current real-time data in the production systemwith historical data being used as real-time data in the test system.But maintaining two separate environments can increase system costsbecause hardware and software are replicated to build the simulationenvironment. There can also be additional staff costs for maintainingand synchronizing two separate environments. It is also unlikely thatevery user of the separate simulation system would want the same set ofdata or same configuration, requiring more staff time for setup andconfiguration. Also, because each user may desire a different simulationconfiguration, simulation resources would have to be reserved for eachuser. The core problem is that production and simulation systemstypically require different code. A person is typically required totranslate between the two systems. This process is error prone and leadsto problems.

Embodiments were conceived in light of the above mentioned needs,problems and/or limitations, among other things.

Some implementation can include a computer system for executing queryprograms in a simulated mode comprising one or more processors, computerreadable storage coupled to the one or more processors, the computerreadable storage having stored thereon instructions that, when executedby the one or more processors, cause the one or more processors toperform operations. The operations can include establishing a digitalconnection between a query processor and a query client device. Theoperations can also include the query client device parsing a queryprogram with one or more configuration instructions. The operations canfurther include receiving at the query processor, a query program withone or more configuration instructions from the query client device. Theoperations can include the query processor parsing the one or moreconfiguration instructions. The operations can also include the queryprocessor determining from the one or more configuration instructionsfrom the query client device whether to initialize a simulation mode ora real-time mode. The operations can include when the query processordetermines a simulation mode, initializing the computer system tooperate in the simulated mode. The initializing can include extracting asimulation period from the one or more configuration instructions. Theinitializing can also include the query processor processing the queryprogram with real-time code. The processing can also include requestingreal-time data simulated from historical data.

The processing can include requesting non-action system generatedhistorical data. The initializing can further include creatinganti-look-ahead bias historical filters.

The processing can further include applying the anti-look-ahead biashistorical data filters to the requested non-action system generatedreal-time data simulated from historical data.

The operations can include when a simulated real-time action system doesnot exist, constructing a simulated real-time action system. Theoperations can also include the query processor connecting to thesimulated real-time action system. The operations can further includegenerating simulated real-time action system data.

The operations can include when a simulated real-time action system doesnot exist, constructing a simulated real-time action system. Theoperations can also include the query processor connecting to thesimulated real-time action system. The operations can further includegenerating simulated real-time action system data.

The operations can include the query processor determining from theparsing of the one or more configuration instructions, a simulationclock cycle and a clock cycle speed. The operations can also include foreach clock cycle for the simulation period, the query processor startinga simulated clock cycle, determining any data changes in the clockcycle, applying the data changes to an update propagation graph, andupdating dynamic simulated real-time action system dynamic tables.

The operations can include wherein real-time data simulated fromhistorical data includes sorting the data by sequence ID or by timestamp for only the current clock cycle prior to use.

Some implementations can include a method for initializing a computersystem to execute query programs in a simulated mode comprisingestablishing a digital connection between a query processor and a queryclient device. The method can also include receiving at the queryprocessor, a query program with one or more configuration instructionsfrom the query client device. The method can further include the queryprocessor parsing the one or more configuration instructions. The methodcan also include the query processor determining from the one or moreconfiguration instructions from the query client device whether toinitialize a simulation mode or a real-time mode. The method can includewhen the query processor determines a simulation mode, initializing thecomputer system to operate in the simulated mode. The initialization caninclude extracting the simulation period from the one or moreconfiguration instructions. The initialization can also include thequery processor processing the query program with real-time code. Theprocessing can also include requesting real-time data simulated fromhistorical data.

The processing can include requesting non-action system generatedhistorical data. The initializing can further include creatinganti-look-ahead bias historical filters.

The processing can also include applying the anti-look-ahead biashistorical data filters to the requested non-action system generatedreal-time data simulated from historical data.

The method can further include when a simulated real-time action systemdoes not exist, constructing a simulated real-time action system. Themethod can also include the query processor connecting to the simulatedreal-time action system. The method can include generating simulatedreal-time action system data.

The method can further include the query processor determining from theparsing of the one or more configuration instructions, a simulationclock cycle and a clock cycle speed. The method can also include foreach clock cycle for the simulation period, the query processor startinga simulated clock cycle, determining any data changes in the clockcycle, applying the data changes to an update propagation graph, andupdating dynamic simulated real-time action system dynamic tables.

The method can further include wherein real-time data simulated fromhistorical data includes sorting the data by sequence ID or by timestamp for only the current clock cycle prior to use.

The method can also include replacing the simulated real-time actionsystem with a second simulated real-time action system.

Some implementations can include a nontransitory computer readablemedium having stored thereon software instructions that, when executedby one or more processors, cause the one or more processors to performoperations. The operations can include establishing a digital connectionbetween a query processor and a query client device. The operations canalso include the query client device parsing a query program with one ormore configuration instructions. The operations can further includereceiving at the query processor, a query program with one or moreconfiguration instructions from the query client device. The operationscan also include the query processor parsing the one or moreconfiguration instructions. The operations can include the queryprocessor determining from the one or more configuration instructionsfrom the query client device whether to initialize a simulation mode ora real-time mode. The operations can also include when the queryprocessor determines a simulation mode, initializing the computer systemto operate in the simulated mode. The initializing can includeextracting a simulation period from the one or more configurationinstructions. The initializing can also include the query processorprocessing the query program with real-time code. The processing canalso include requesting real-time data simulated from historical data.

The processing can include requesting non-action system generatedhistorical data. The initializing can further include creatinganti-look-ahead bias historical filters.

The processing can also include applying the anti-look-ahead biashistorical data filters to the requested non-action system generatedreal-time data simulated from historical data.

The operations can include when a simulated real-time action system doesnot exist, constructing a simulated real-time action system. Theoperations can also include the query processor connecting to thesimulated real-time action system. The operations can further includegenerating simulated real-time action system data.

The operations can include when a simulated real-time action system doesnot exist, constructing a simulated real-time action system. Theoperations can also include the query processor connecting to thesimulated real-time action system. The operations can further includegenerating simulated real-time action system data.

The operations can include the query processor determining from theparsing of the one or more configuration instructions, a simulationclock cycle and a clock cycle speed. The operation can also include foreach clock cycle for the simulation period, the query processor startinga simulated clock cycle, determining any data changes in the clockcycle, applying the data changes to an update propagation graph, andupdating dynamic simulated real-time action system dynamic tables.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example computer data system showing anexample data distribution configuration in accordance with someimplementations.

FIG. 2 is a diagram of an example computer data system showing anexample administration/process control arrangement in accordance withsome implementations.

FIG. 3 is a diagram of an example computing device configured for datareplay processing in accordance with some implementations.

FIG. 4 is a diagram of an example system in real-time mode in accordancewith some implementations.

FIG. 5 is a diagram of an example system in simulation mode inaccordance with some implementations.

FIG. 5A is a diagram of an example update propagation graph in real-timemode.

FIG. 5B is a diagram of an example update propagation graph insimulation mode.

FIG. 6 is a flowchart of an example system initialization in accordancewith some implementations.

FIG. 7 is a flowchart of an example data replay simulation in accordancewith some implementations.

DETAILED DESCRIPTION

Reference is made herein to the Java programming language, Java classes,Java bytecode and the Java Virtual Machine (JVM) for purposes ofillustrating example implementations. It will be appreciated thatimplementations can include other programming languages (e.g., groovy,Scala, R, Go, etc.), other programming language structures as analternative to or in addition to Java classes (e.g., other languageclasses, objects, data structures, program units, code portions, scriptportions, etc.), other types of bytecode, object code and/or executablecode, and/or other virtual machines or hardware implemented machinesconfigured to execute a data system query.

FIG. 1 is a diagram of an example computer data system and network 100showing an example data distribution configuration in accordance withsome implementations. In particular, the system 100 includes anapplication host 102, a periodic data import host 104, a query serverhost 106, a long-term file server 108, and a user data import host 110.While tables are used as an example data object in the descriptionbelow, it will be appreciated that the data system described herein canalso process other data objects such as mathematical objects (e.g., asingular value decomposition of values in a given range of one or morerows and columns of a table), TableMap objects, etc. A TableMap objectprovides the ability to lookup a Table by some key. This key representsa unique value (or unique tuple of values) from the columns aggregatedon in a byExternal( ) statement execution, for example. A TableMapobject can be the result of a byExternal( ) statement executed as partof a query. It will also be appreciated that the configurations shown inFIGS. 1 and 2 are for illustration purposes and in a givenimplementation each data pool (or data store) may be directly attachedor may be managed by a file server.

The application host 102 can include one or more application processes112, one or more log files 114 (e.g., sequential, row-oriented logfiles), one or more data log tailers 116 and a multicast key-valuepublisher 118. The periodic data import host 104 can include a localtable data server, direct or remote connection to a periodic table datastore 122 (e.g., a column-oriented table data store) and a data importserver 120. The query server host 106 can include a multicast key-valuesubscriber 126, a performance table logger 128, local table data store130 and one or more remote query processors (132, 134) each accessingone or more respective tables (136, 138). The long-term file server 108can include a long-term data store 140. The user data import host 110can include a remote user table server 142 and a user table data store144. Row-oriented log files and column-oriented table data stores arediscussed herein for illustration purposes and are not intended to belimiting. It will be appreciated that log files and/or data stores maybe configured in other ways. In general, any data stores discussedherein could be configured in a manner suitable for a contemplatedimplementation.

In operation, the input data application process 112 can be configuredto receive input data from a source (e.g., a securities trading datasource), apply schema-specified, generated code to format the loggeddata as it's being prepared for output to the log file 114 and store thereceived data in the sequential, row-oriented log file 114 via anoptional data logging process. In some implementations, the data loggingprocess can include a daemon, or background process task, that isconfigured to log raw input data received from the application process112 to the sequential, row-oriented log files on disk and/or a sharedmemory queue (e.g., for sending data to the multicast publisher 118).Logging raw input data to log files can additionally serve to provide abackup copy of data that can be used in the event that downstreamprocessing of the input data is halted or interrupted or otherwisebecomes unreliable.

A data log tailer 116 can be configured to access the sequential,row-oriented log file(s) 114 to retrieve input data logged by the datalogging process. In some implementations, the data log tailer 116 can beconfigured to perform strict byte reading and transmission (e.g., to thedata import server 120). The data import server 120 can be configured tostore the input data into one or more corresponding data stores such asthe periodic table data store 122 in a column-oriented configuration.The periodic table data store 122 can be used to store data that isbeing received within a time period (e.g., a minute, an hour, a day,etc.) and which may be later processed and stored in a data store of thelong-term file server 108. For example, the periodic table data store122 can include a plurality of data servers configured to store periodicsecurities trading data according to one or more characteristics of thedata (e.g., a data value such as security symbol, the data source suchas a given trading exchange, etc.).

The data import server 120 can be configured to receive and store datainto the periodic table data store 122 in such a way as to provide aconsistent data presentation to other parts of the system.Providing/ensuring consistent data in this context can include, forexample, recording logged data to a disk or memory, ensuring rowspresented externally are available for consistent reading (e.g., to helpensure that if the system has part of a record, the system has all ofthe record without any errors), and preserving the order of records froma given data source. If data is presented to clients, such as a remotequery processor (132, 134), then the data may be persisted in somefashion (e.g., written to disk).

The local table data server 124 can be configured to retrieve datastored in the periodic table data store 122 and provide the retrieveddata to one or more remote query processors (132, 134) via an optionalproxy.

The remote user table server (RUTS) 142 can include a centralizedconsistent data writer, as well as a data server that providesprocessors with consistent access to the data that it is responsible formanaging. For example, users can provide input to the system by writingtable data that is then consumed by query processors.

The remote query processors (132, 134) can use data from the data importserver 120, local table data server 124 and/or from the long-term fileserver 108 to perform queries. The remote query processors (132, 134)can also receive data from the multicast key-value subscriber 126, whichreceives data from the multicast key-value publisher 118 in theapplication host 102. The performance table logger 128 can logperformance information about each remote query processor and itsrespective queries into a local table data store 130. Further, theremote query processors can also read data from the RUTS, from localtable data written by the performance logger, or from user table dataread over NFS.

It will be appreciated that the configuration shown in FIG. 1 is atypical example configuration that may be somewhat idealized forillustration purposes. An actual configuration may include one or moreof each server and/or host type. The hosts/servers shown in FIG. 1(e.g., 102-110, 120, 124 and 142) may each be separate or two or moreservers may be combined into one or more combined server systems. Datastores can include local/remote, shared/isolated and/or redundant. Anytable data may flow through optional proxies indicated by an asterisk oncertain connections to the remote query processors. Also, it will beappreciated that the term “periodic” is being used for illustrationpurposes and can include, but is not limited to, data that has beenreceived within a given time period (e.g., millisecond, second, minute,hour, day, week, month, year, etc.) and which has not yet been stored toa long-term data store (e.g., 140).

FIG. 2 is a diagram of an example computer data system 200 showing anexample administration/process control arrangement in accordance withsome implementations. The system 200 includes a production client host202, a controller host 204, a GUI host or workstation 206, and queryserver hosts 208 and 210. It will be appreciated that there may be oneor more of each of 202-210 in a given implementation.

The production client host 202 can include a batch query application 212(e.g., a query that is executed from a command line interface or thelike) and a real time query data consumer process 214 (e.g., anapplication that connects to and listens to tables created from theexecution of a separate query). The batch query application 212 and thereal time query data consumer 214 can connect to a remote querydispatcher 222 and one or more remote query processors (224, 226) withinthe query server host 1 208.

The controller host 204 can include a persistent query controller 216configured to connect to a remote query dispatcher 232 and one or moreremote query processors 228-230. In some implementations, the persistentquery controller 216 can serve as the “primary client” for persistentqueries and can request remote query processors from dispatchers, andsend instructions to start persistent queries. For example, a user cansubmit a query to 216, and 216 starts and runs the query every day. Inanother example, a securities trading strategy could be a persistentquery. The persistent query controller can start the trading strategyquery every morning before the market open, for instance. It will beappreciated that 216 can work on times other than days. In someimplementations, the controller may require its own clients to requestthat queries be started, stopped, etc. This can be done manually, or byscheduled (e.g., cron) jobs. Some implementations can include “advancedscheduling” (e.g., auto-start/stop/restart, time-based repeat, etc.)within the controller.

The GUI/host workstation can include a user console 218 and a user queryapplication 220. The user console 218 can be configured to connect tothe persistent query controller 216. The user query application 220 canbe configured to connect to one or more remote query dispatchers (e.g.,232) and one or more remote query processors (228, 230).

FIG. 3 is a diagram of an example computing device 300 in accordancewith at least one implementation. The computing device 300 includes oneor more processors 302, operating system 304, computer readable medium306 and network interface 308. The memory 306 can include remote queryprocessor application 310 and a data section 312 (e.g., for storingASTs, precompiled code, etc.).

In operation, the processor 302 may execute the application 310 storedin the memory 306. The application 310 can include software instructionsthat, when executed by the processor, cause the processor to performoperations for historical data replay operations in accordance with thepresent disclosure (e.g., performing one or more of 602-626, 702-716described below).

The application program 310 can operate in conjunction with the datasection 312 and the operating system 304.

Large data-dependent systems such as real-time stock trading systems canreceive, parse, and analyze a continuous large stream of data that canbe stored into large historical data stores for further analysis orreplay. A user may desire to replay all, or a subset of a particulartrading day's data at a later point to determine if better trades couldhave been made or if the user's queries being used to make tradingdecisions were optimal or whether a modification to the queries wouldhave provided better decision making results and thus better trades.Setting up a separate simulation environment that replays historicaltrading data is difficult and error-prone. Moreover, anincorrectly-provisioned simulation environment may not accuratelyrepresent the state of the data at any given point in time. Beyondconcerns relating to either the fidelity or ease-of-use of a simulationenvironment, when separate query code is employed in simulation andproduction, costly errors can be made when translating from oneenvironment to another. The user would rather have the option to submita simple command, well within a basic user's query writing skillset, andsubmit that command to the computer system, such as RunSimulation2016-03-03. A RunSimulation 2016-03-03 command could alert the systemthat the user desires that the user's script be executed using theexisting computer system but with data collected from 2016-03-03 assimulated real-time input instead of current real-time data.

FIG. 4 is a diagram of an example computer system in real-time mode 400in accordance with some implementations. The query client 410 can beused to submit a query script to a computer system. The query client 410can be on a remote computing device or local computing device. The queryprocessor 408 can receive the submitted query script and start theprocessing of the query script. If the query script requires historicaldata 414, the query processor 408 can retrieve the appropriatehistorical data 414. The historical data 414 can be stored data that waspreviously collected as real-time data 402 or data that was neveravailable in a real-time environment. If the query script requiresreal-time data 402, the query processor 408 can retrieve the appropriatereal-time data 402. The real-time data 402 can be continuously updatedwith current real-time data streams from a real-time action system 404and an external real-time data source 401. The query processor 408 cancommunicate with a real-time action system 404.

It will be appreciated that real-time data can be collected at differentrates that can depend on the availability of data from the externalreal-time data source 401 and the real-time action system 404.

It will also be appreciated that the computer system can be a liveproduction computer system or the computer system can be a separatesystem from the live production computer system that is running the samecode. It will also be appreciated that the historical data 414 can bethe same source for both types of systems.

FIG. 5 is a diagram of an example computer system in simulation mode 500in accordance with some implementations. A query client 410 can be thesame query client for the system in production mode in FIG. 4. A queryprocessor 408 can be the same query processor for the system inproduction mode in FIG. 4. A historical data 414 can be the samehistorical data 414 store for the system in production mode in FIG. 4.In simulation mode 500, the real-time data 402 in FIG. 4 can be replacedby real-time data simulated from historical data 502. For example, iftoday's date is 2016-03-15 and a user submitted a simulation request toreplay data from 2016-03-13, the system can set up a portion of thehistorical data 414 from 2016-03-13 to be used as real-time datasimulated from historical data 502 to be replayed into the system as ifit were real-time data occurring on 2016-03-13. The distinction betweenhistorical data 414 and real-time data simulated from historical data502 can be in identifying the boundaries of the historical data to beused as real-time data. For example, the data identified as real-timedata simulated from historical data 502 is only a designation of asection of historical data 414 to be used in a simulation as real-timedata for a period of time. The designation, in itself, does not alterthe underlying historical data 414 that is cordoned off as data to beused in a simulation. Any changes to the real-time data simulated fromhistorical data 502 in preparation to use the data for the simulationwould occur after the data is read from the data store. The underlyinghistorical data 414 remains unchanged. A simulated real-time actionsystem 504 can replace the real-time action system 404 in FIG. 4. Datarequired to simulate a real-time action system 510 can be retrieved by aquery from the real-time data simulated from historical data 502. Asimulated results and state log 508 can capture logged events from asimulation run.

It will be appreciated that the real-time data received from an externalreal-time data source 401 in FIG. 4 is not needed in simulation mode 500because the real-time data 402 received from an external real-time datasource 401 can be simulated with real-time data simulated fromhistorical data 502. For example, external real-time data source 401 canbe a provider that streams stock market quotes with date-timestamps as aservice. The stock market quotes with associated date-timestamps can besaved into a historical data 414 store for later replay into asimulation mode. Alternatively, a third-party source of data can bepurchased and inserted into the historical data 502 for replay as if itwere available in real-time.

It will also be appreciated that a real-time action system 404 can be anexternal system that can communicate with a query processor 408 to carryout user requests. For example, a real-time action system 404 can be astock trading entity that can receive buy or sell commands for aparticular stock from the query processor 408 based on a user queryscript from a query client 410. The simulated real-time action system504 can fill stock orders in a manner that is consistent with theobserved behavior of the real-time action system, for example the user'ssimulated portfolio being modified in the same manner as the real-timeaction system.

It will further be appreciated that the query processor 408 can access asimulated results and state log 508 to provide a query with knowledge ofhow the query has impacted simulated reality. For example, changes inposition data updates as a result of simulated trades.

FIG. 5A is a diagram of an example Directed Acyclic Graph (update querygraph) in real-time mode 520. For example, in real-time mode 520, astatic t1 table object indexed to historical data 522 can be joinedusing a join operation 526 with a dynamic t2 table object indexed toreal-time data 524 to form a dynamic t3 table object 528. New real-timedata 530 can be added to the dynamic t2 table object indexed toreal-time data 524 and the new real-time data 530 can be propagateddownward through the update propagation graph to update the child nodes,such as the join operation 526 and the dynamic t3 table object 528.

FIG. 5B is a diagram of an example update propagation graph insimulation mode 540. For example, in simulation mode, a static t4 tableobject indexed to historical data 546 can be joined using a joinoperation 550 with a dynamic t5 table object indexed to simulatedreal-time data 548 to form a dynamic t6 table object 552. Historicaldata can be filtered by an anti-look ahead bias filtering 542 to preventusers from being able to look ahead at data that can occur at a futuresimulation time. New simulated real-time data filtered by simulatedreal-time data historical filters 541 can be added to the dynamic t5table object indexed to simulated real-time data 548 and an optionalsorting 544 is applied, and the new real-time data 541 can be propagateddownward through the update propagation graph to update the child nodes,such as the join operation 550 and the dynamic t6 table object 552.

FIG. 6 is a flowchart of an example computer system initialization 600in accordance with some implementations. Processing begins at 602, whenthe computer system query processor 408 receives a query script from aquery client 410. Processing continues to 604.

At 604, the computer system can determine whether the query scriptdesignates a real-time or simulation mode. For example, a RunSimulation2010-04-15 2016-03-03 myscript.extension can be interpreted asdesignating a simulation mode and a Run myscript.extension can beinterpreted as designating a real-time mode. If a real-time mode isdesignated, processing continues to 606. If a simulation mode isdesignated, processing continues to 612. The 606 path is discussed firstbefore returning to discuss the 612 path.

At 606, the computer system can connect to a real-time action system606. Such a connection is shown in FIG. 4 between a real-time actionsystem 404 and a query processor 408. Processing continues to 608.

At 608, a query processor 408 executes the received query script usingthe real-time system code. For example, if the query script containst3=t1.where(“SYM=‘AAPL’”), the query processor 408 can build an updategraph containing a t1 dynamic table object node, a where operation childnode, and a resultant t3 dynamic table object child node. Processingcontinues to 610.

At 610, a query processor 408 retrieves the historical data 414 andreal-time data 402 without modifying the data to complete the queryscript. For example, if the query script contains t3=t1.join(t2, “SYM”),and t1 is a table object with data based on historical data 414 and a t2object is a dynamic table being updated every second with real-time data402, the query processor 408 can retrieve data from real-time data 402and historical data 414 to complete the join to create the dynamic t3table object. In this example, the dynamic t3 table object will continueto be updated as new data is received from the real-time data 402 bylistening for changes to the parent table t2. The discussion now returnsto the simulation path from 604 to the processing at 612.

At 612, the query processor 408 parses the query script commandsubmission in order to identify the time period to simulate. Forexample, a “RunSimulation 2010-04-15 2016-03-03 myscript.extension” canbe parsed to obtain a period starting with 2010-04-15 and ending with2016-03-03.

It will be appreciated that a client device can also parse a queryscript command prior to submission. Processing continues to 614.

At 614, the query processor 408 creates anti-look-ahead bias historicaldata filters. For example, if the simulation period is from 2010-04-15to 2016-03-03, the filters can be created to keep the processing of datastarting with 2010-04-15 from looking ahead to data from a later timeuntil that later time clock cycle has occurred.

It will be appreciated that an anti-look ahead bias filter can preventaccess to historical data that has not yet occurred in the simulationand can operate on a gross time-period, for example, one day. Processingcontinues to 616.

At 616, the query processor 408 creates simulated real-time datahistorical filters for the real-time data simulated from historical data502. The historical filters can be applied to each day of simulatedtime. For example, if the simulation period is from 2010-04-15 to2016-03-03, a real-time data historical filter can be configured foreach of the days from 2010-04-15 to 2016-03-03 to confine the retrievalof data from the historical data 414 to data date-timestamps for aparticular time of the simulation between 2010-04-14 and 2016-03-04 inorder to keep the user from accessing data from a point in time beyondthe current simulated time, which would be future data in thesimulation.

It will be appreciated that simulated real-time data historical filterscan be more fine-grained than an anti-look ahead bias filter. Forexample, a simulated real-time data historical filter can be for atime-period within a particular day time-period for preventing a userfrom accessing data from a later time in a same simulation day (e.g.,preventing access to a 4 o'clock exchange closing price on a stock whenthe current simulation time is at 9:30 a.m.). The simulated real-timehistorical filter can permit the system to trickle in data as thesimulation clock advances. Processing continues to 618.

At 618, a query processor 408 constructs a simulated real-time actionsystem 504 if one does not already exist. If a simulated real-timeaction system 504 already exists, the query processor 408 can connect tothe simulated real-time action system 504.

It will be appreciated that a simulated real-time action system can bereplaced by another simulated real-time action system that may havedifferent characteristics. Processing continues to 620.

At 620, a query processor 408 executes the received query script usingthe production system code. For example, if the query script containst3=t1.where(“SYM=‘AAPL’”), the query processor 408 can build an updategraph containing a t1 dynamic table object node, a where operation node,and a resultant t3 dynamic table object node. The update graph is nowset up to permit each table object to update as simulated new data isprocessed through the running of the simulation.

It will be appreciated that the query processor 408 is executing thesame code in the simulation path from 604 as the query processor 408uses in the execution of the query script in real-time mode in 608.

It will be appreciated that a difference is not found in the source ofcode being executed but instead in the source for real-time data duringthe simulation, and in the historical data in the sense that ananti-look-ahead filter can be applied. Processing continues to 622.

At 622, if a non-action system generated historical table is requestedby a query processor 408 to execute the query script, theanti-look-ahead bias historical filters are applied before returning thequery results. For example, if t1 is a table object populated with datafrom historical data 414 as part of the simulation, and the simulatedtime period is from 2010-04-15 to 2016-03-03 and the simulation clock isat 2010-04-15, the anti-look-ahead bias historical filters are appliedto keep any of the historical data 414 with a date-timestamp after2010-04-14 from being accessed as historical data because in thesimulation the data after 2010-04-14 does not yet exist. Processingcontinues to 624.

At 624, if a real-time table is requested by a query processor 408 toexecute a query script, the simulated real-time data historical filteris applied to the historical data 414. The filtered historical data canthen be converted into real-time data simulated from historical data 502to be used in the simulation as real-time data. The retrieved data canbe optionally sorted, for example, by sequence ID or time stamp in orderto remedy any situations where the data was not stored in the same orderit was generated in real-time. For example, if t2 is a dynamic tableobject populated with simulated real-time data as part of thesimulation, the query processor 408 can update the t2 table object asthe simulation ticks through the clock cycles with the correspondingslice of simulated real-time data from the historical data 414.

It will be appreciated that the optional sort can be performed on onlythe data that is necessary to perform the next clock cycle prior to thestart of the clock cycle. Processing continues to 626.

At 626, if a real-time table from the simulated real-time action systemis requested by a query processor 408 to execute a query script, therelevant real-time table is retrieved from a location specified by thesimulated real-time action system. For example, the location may be ahandle to an in-memory table, a handle to an on disk table, etc. Thesimulated real-time action system tables typically contain stateinformation for the simulated real-time action system such as currentposition sizes, current orders in the market, and the like. The tablesare dynamic and can depend on the history of a particular real-timeaction system instance.

It will be appreciated that a role of the simulated real-time actionsystem 504 can be to provide action system results to a user and acceptuser requests from the query script through interactions with a queryprocessor 408. For example, if the real-time action system 404 is astock trading system capable of accepting and acting on buy and sellorders, a user, who has an account with the stock trading system, caninstruct the stock trading system through the query processor 408 topurchase a thousand shares of AAPL stock at limit price of $100 a share.If the conditions are met and the stock trading system completes therequested trade, the stock trading system adds the purchased stockshares to the user's portfolio that is maintained by the stock tradingsystem and informs the user through the query processor 408 that thetransaction has completed and reports the total number of shares(position) of AAPL that the user has in the portfolio. The position datacan also be stored in the historical data 414 after being received asreal-time data 402. During the simulation, the positions of the user canbe rolled back to the positions at the start of the simulation time andthen increased or decreased by actions taken by the user's query scriptsduring the simulation while at the same time stripping out the ordersthat were made and completed during the actual real-time period. Thepositions can also be set to zero or another arbitrary number to startthe simulation.

FIG. 7 is a flowchart of an example data replay simulation 700 in thecomputer system in accordance with some implementations. Processingbegins at 702.

At 702, the speed of the simulated clock cycle is determined by thequery processor 408. For example, the user can submit a parameter withthe RunSimulation program or GUI that specifies the clock cycle timingin relation to real time. The user can choose to run the simulation inslow motion or faster than one second of simulation per one second ofmeasured time. The user can run the simulation as fast as the system canprocess the data cycles. Processing continues to 704.

At 704, the query processor 408 begins the simulated clock cycle.Processing continues to 706.

At 706, data of real-time data simulated from historical data 502corresponding to the time parameters of the current simulated clockcycle is loaded into each simulated real-time data source. For example,if dynamic table objects t1, t2, and t3 are created from real-time datasimulated from historical data 502, then a update propagation graph noderepresentation is created for each of the table objects in a queryprocessor memory. Then these table objects can be used to determinewhich data of real-time data simulated from historical data 502 areneeded for the current simulated clock cycle.

It will be appreciated that each of the dynamic (real-time) tableobjects in existence at the time of the simulated time period cancontain an index in the update propagation graph that can provide amapping to the real-time data simulated from historical data 502 stores.Processing continues to 708.

At 708, each of the data loaded in 706 can be optionally sorted by asequence ID or time stamp associated with each row of data.

It will be appreciated that sorting may not be needed if data waswritten to a historical data 414 store in the same order as it wascreated. The sorting can be applied for instances where data can bewritten to the historical data 414 out of order. Sorting may also not berequired if a user script does not require the results to be sorted.Processing continues to 710.

At 710, the possibly ordered data for the clock cycle are processedthrough the update propagation graph as data changes. For example, ifdynamic table objects t1, t2, and t3 are dynamic table objectsrepresented in a update propagation graph with table object t2 being achild of table object t1 and table object t3 being a child of tableobject t2, the processing of the data through the update propagationgraph starts with the parent table object node, t1. To further theexample, table object t1 is a dynamic table object that can contain allthe rows before the current simulation time from a real-time data sourcestored on a fileserver; table object t2 is a dynamic table object thatcan contain all of the rows from table object t1 that contain the AAPLstock symbol; and table object t3 is a dynamic table object that cancontain all of the rows from t2 that have a stock price greater than$100. At the start of the clock cycle, all of the new rows from the datathat were added to the stored data source can be added to the t1 tableobject because the t1 table object is a dynamic table that can beupdated when its data source is updated. Because table object t2, achild table object of table object t1, can listen for updates to tableobject t1, when updates occur to table object t1, table object t2 can beupdated. And because table object t3, a child object of table object t2,can listen for updates to table object t2, when updates occur to tableobject t2, table object t3 can be updated. Processing continues to 711.

At 711, update simulated real-time action system. Processing continuesto 712.

At 712, results from simulated actions taken by a simulated real-timeaction system 504 in response to user's query scripts submitted to aquery processor 408 and passed onto the simulated real-action system 504can be written to simulated results and state logs 508. For example, ifthe query processor 408 determines the conditions from the query scriptare met for following a strategy for purchasing stock shares, a requestwith or without limitation instructions can be sent by the queryprocessor 408 to a simulated real-time action system 504 to purchase thestock shares. The simulated real-time action system 504 can then look atthe real-time simulated conditions at the simulation time of the requestand determine if the request can be filled or not filled. Thedetermination of the simulated real-time action system 504 can then bewritten to a simulated results and state logs 508 for further analysisas to why the trade was filled or why the trade was not filled. Theresults and state logs 508 can also be analyzed for the profitability ofthe decisions, and also provide feedback to the simulation query as itcontinues. Processing continues to 714.

At 714, dynamic position tables from a simulated real-time action system504 can be updated with the outcome of a transaction. For example, ifthe simulated real-time action system is successful in purchasing athousand shares of stock for the user, the user's portfolio is updatedwith the new position data.

It will be appreciated that simulated real-time action system tablesderived from historical tables can be transformed according toinstructions or specifications from the real time action system.Processing continues to 716.

At 716, if the clock cycle is still within the simulation time-period,the process proceeds to the next clock cycle and returns to step 704 torepeat steps 704 through 716. The loop can continue until the end of thesimulation time-period is reached.

It will be appreciated that multiple period-centric loops can beimplemented for a simulation time-period such as multiple day-centricloops that can extend over several days. For example, a 15-daysimulation period can be broken down into 15 separate one-daysimulations. It will also be appreciated breaking a simulation periodinto multiple separate simulations can affect a maintenance of continualstate and feedback.

It will be appreciated that the modules, processes, systems, andsections described above can be implemented in hardware, hardwareprogrammed by software, software instructions stored on a nontransitorycomputer readable medium or a combination of the above. A system asdescribed above, for example, can include a processor configured toexecute a sequence of programmed instructions stored on a nontransitorycomputer readable medium. For example, the processor can include, butnot be limited to, a personal computer or workstation or other suchcomputing system that includes a processor, microprocessor,microcontroller device, or is comprised of control logic includingintegrated circuits such as, for example, an Application SpecificIntegrated Circuit (ASIC), a field programmable gate array (FPGA),graphics processing unit (GPU), or the like. The instructions can becompiled from source code instructions provided in accordance with aprogramming language such as Java, C, C++, C#.net, assembly or the like.The instructions can also comprise code and data objects provided inaccordance with, for example, the Visual Basic™ language, a specializeddatabase query language, or another structured or object-orientedprogramming language. The sequence of programmed instructions, orprogrammable logic device configuration software, and data associatedtherewith can be stored in a nontransitory computer-readable medium suchas a computer memory or storage device which may be any suitable memoryapparatus, such as, but not limited to ROM, PROM, EEPROM, RAM, flashmemory, disk drive and the like.

Furthermore, the modules, processes systems, and sections can beimplemented as a single processor or as a distributed processor.Further, it should be appreciated that the steps mentioned above may beperformed on a single or distributed processor (single and/ormulti-core, or cloud computing system). Also, the processes, systemcomponents, modules, and sub-modules described in the various figures ofand for embodiments above may be distributed across multiple computersor systems or may be co-located in a single processor or system. Examplestructural embodiment alternatives suitable for implementing themodules, sections, systems, means, or processes described herein areprovided below.

The modules, processors or systems described above can be implemented asa programmed general purpose computer, an electronic device programmedwith microcode, a hard-wired analog logic circuit, software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, an integrated circuit device, a semiconductor chip,and/or a software module or object stored on a computer-readable mediumor signal, for example.

Embodiments of the method and system (or their sub-components ormodules), may be implemented on a general-purpose computer, aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element, an ASIC or other integratedcircuit, a digital signal processor, a hardwired electronic or logiccircuit such as a discrete element circuit, a programmed logic circuitsuch as a PLD, PLA, FPGA, PAL, or the like. In general, any processorcapable of implementing the functions or steps described herein can beused to implement embodiments of the method, system, or a computerprogram product (software program stored on a nontransitory computerreadable medium).

Furthermore, embodiments of the disclosed method, system, and computerprogram product (or software instructions stored on a nontransitorycomputer readable medium) may be readily implemented, fully orpartially, in software using, for example, object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer platforms. Alternatively,embodiments of the disclosed method, system, and computer programproduct can be implemented partially or fully in hardware using, forexample, standard logic circuits or a VLSI design. Other hardware orsoftware can be used to implement embodiments depending on the speedand/or efficiency requirements of the systems, the particular function,and/or particular software or hardware system, microprocessor, ormicrocomputer being utilized. Embodiments of the method, system, andcomputer program product can be implemented in hardware and/or softwareusing any known or later developed systems or structures, devices and/orsoftware by those of ordinary skill in the applicable art from thefunction description provided herein and with a general basic knowledgeof the software engineering and computer networking arts.

Moreover, embodiments of the disclosed method, system, and computerreadable media (or computer program product) can be implemented insoftware executed on a programmed general purpose computer, a specialpurpose computer, a microprocessor, or the like.

It is, therefore, apparent that there is provided, in accordance withthe various embodiments disclosed herein, methods, systems and computerreadable media for simulated replay of data using a computer system.

Application Ser. No. 15/154,974, entitled “DATA PARTITIONING ANDORDERING” (Attorney Docket No. W1.1-10057) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,975, entitled “COMPUTER DATA SYSTEM DATASOURCE REFRESHING USING AN UPDATE PROPAGATION GRAPH” (Attorney DocketNo. W1.4-10058) and filed in the United States Patent and TrademarkOffice on May 14, 2016, is hereby incorporated by reference herein inits entirety as if fully set forth herein.

Application Ser. No. 15/154,979, entitled “COMPUTER DATA SYSTEMPOSITION-INDEX MAPPING” (Attorney Docket No. W1.5-10083) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,980, entitled “SYSTEM PERFORMANCE LOGGING OFCOMPLEX REMOTE QUERY PROCESSOR QUERY OPERATIONS” (Attorney Docket No.W1.6-10074) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,983, entitled “DISTRIBUTED AND OPTIMIZEDGARBAGE COLLECTION OF REMOTE AND EXPORTED TABLE HANDLE LINKS TO UPDATEPROPAGATION GRAPH NODES” (Attorney Docket No. W1.8-10085) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,984, entitled “COMPUTER DATA SYSTEM CURRENTROW POSITION QUERY LANGUAGE CONSTRUCT AND ARRAY PROCESSING QUERYLANGUAGE CONSTRUCTS” (Attorney Docket No. W2.1-10060) and filed in theUnited States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,985, entitled “PARSING AND COMPILING DATASYSTEM QUERIES” (Attorney Docket No. W2.2-10062) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,987, entitled “DYNAMIC FILTER PROCESSING”(Attorney Docket No. W2.4-10075) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,988, entitled “DYNAMIC JOIN PROCESSING USINGREAL-TIME MERGED NOTIFICATION LISTENER” (Attorney Docket No. W2.6-10076)and filed in the United States Patent and Trademark Office on May 14,2016, is hereby incorporated by reference herein in its entirety as iffully set forth herein.

Application Ser. No. 15/154,990, entitled “DYNAMIC TABLE INDEX MAPPING”(Attorney Docket No. W2.7-10077) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,991, entitled “QUERY TASK PROCESSING BASEDON MEMORY ALLOCATION AND PERFORMANCE CRITERIA” (Attorney Docket No.W2.8-10094) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,993, entitled “A MEMORY-EFFICIENT COMPUTERSYSTEM FOR DYNAMIC UPDATING OF JOIN PROCESSING” (Attorney Docket No.W2.9-10107) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,995, entitled “QUERY DISPATCH AND EXECUTIONARCHITECTURE” (Attorney Docket No. W3.1-10061) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,996, entitled “COMPUTER DATA DISTRIBUTIONARCHITECTURE” (Attorney Docket No. W3.2-10087) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,997, entitled “DYNAMIC UPDATING OF QUERYRESULT DISPLAYS” (Attorney Docket No. W3.3-10059) and filed in theUnited States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,998, entitled “DYNAMIC CODE LOADING”(Attorney Docket No. W3.4-10065) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,999, entitled “IMPORTATION, PRESENTATION,AND PERSISTENT STORAGE OF DATA” (Attorney Docket No. W3.5-10088) andfiled in the United States Patent and Trademark Office on May 14, 2016,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

Application Ser. No. 15/155,001, entitled “COMPUTER DATA DISTRIBUTIONARCHITECTURE” (Attorney Docket No. W3.7-10079) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/155,005, entitled “PERSISTENT QUERY DISPATCH ANDEXECUTION ARCHITECTURE” (Attorney Docket No. W4.2-10089) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/155,006, entitled “SINGLE INPUT GRAPHICAL USERINTERFACE CONTROL ELEMENT AND METHOD” (Attorney Docket No. W4.3-10063)and filed in the United States Patent and Trademark Office on May 14,2016, is hereby incorporated by reference herein in its entirety as iffully set forth herein.

Application Ser. No. 15/155,007, entitled “GRAPHICAL USER INTERFACEDISPLAY EFFECTS FOR A COMPUTER DISPLAY SCREEN” (Attorney Docket No.W4.4-10090) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/155,009, entitled “COMPUTER ASSISTED COMPLETIONOF HYPERLINK COMMAND SEGMENTS” (Attorney Docket No. W4.5-10091) andfiled in the United States Patent and Trademark Office on May 14, 2016,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

Application Ser. No. 15/155,010, entitled “HISTORICAL DATA REPLAYUTILIZING A COMPUTER SYSTEM” (Attorney Docket No. W5.1-10080) and filedin the United States Patent and Trademark Office on May 14, 2016, ishereby incorporated by reference herein in its entirety as if fully setforth herein.

Application Ser. No. 15/155,011, entitled “DATA STORE ACCESS PERMISSIONSYSTEM WITH INTERLEAVED APPLICATION OF DEFERRED ACCESS CONTROL FILTERS”(Attorney Docket No. W6.1-10081) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,012, entitled “REMOTE DATA OBJECTPUBLISHING/SUBSCRIBING SYSTEM HAVING A MULTICAST KEY-VALUE PROTOCOL”(Attorney Docket No. W7.2-10064) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein

While the disclosed subject matter has been described in conjunctionwith a number of embodiments, it is evident that many alternatives,modifications and variations would be, or are, apparent to those ofordinary skill in the applicable arts. Accordingly, Applicants intend toembrace all such alternatives, modifications, equivalents and variationsthat are within the spirit and scope of the disclosed subject matter.

1-20. (canceled)
 21. A computer system for using a productionenvironment to execute query programs in a simulated mode, the systemcomprising: one or more processors; computer readable storage coupled tothe one or more processors, the computer readable storage having storedthereon instructions that, when executed by the one or more processors,cause the one or more processors to perform operations including:establishing a digital connection between a query processor and a queryclient device; receiving real-time data and storing historical data inthe production environment, wherein the real-time data is continuouslyupdated with current real-time data streams from a real-time datasource, and wherein the historical data includes one or more ofpreviously collected real-time data or other data not available in areal-time environment; receiving at the query processor, a first queryprogram with one or more first configuration instructions from the queryclient device; the query processor determining from the one or morefirst configuration instructions from the query client device that areal-time mode is to be used when executing the first query program; thequery processor executing, in the real-time mode, the first queryprogram in the production environment using production system code;receiving at the query processor, a second query program with one ormore second configuration instructions from the query client device; thequery processor parsing the one or more second configurationinstructions; the query processor determining from the one or moresecond configuration instructions from the query client device that asimulation mode is to be used when executing the second query program;applying simulated real-time data historical filters to the historicaldata; converting the filtered historical data into simulated real-timedata; and using the simulated real-time data in a simulation.
 22. Thesystem of claim 21, the operations further comprising: requesting, bythe one or more processors, non-action system generated historical data;and creating, by the one or more processors, anti-look-ahead biashistorical filters.
 23. The system of claim 22, the processing furthercomprising: applying, by the query processor, the anti-look-ahead biashistorical data filters to the requested non-action system generatedreal-time data simulated from historical data.
 24. The system of claim21, the operations further comprising: when a simulated real-time actionsystem does not exist, constructing, by the one or more processors, asimulated real-time action system; the query processor connecting to thesimulated real-time action system; and generating, by the one or moreprocessor, simulated real-time action system data.
 25. The system ofclaim 24, the operations further comprising replacing the simulatedreal-time action system with a second simulated real-time action system.26. The system of claim 21, wherein the real-time data are simulatedfrom historical data by reading a copy of underlying historical data andapplying, after the reading, one or more changes to the read copy ofunderlying historical data; and wherein the underlying historical dataare unchanged by the reading and the applying.
 27. The system of claim21, wherein the simulated real-time data includes the query processorsorting the simulated real-time data prior to use by one or more ofsequence ID or by time stamp.
 28. A method for using a computer systemin a production environment to execute query programs in a simulatedmode, the method comprising: establishing a digital connection between aquery processor and a query client device; the computer system receivingreal-time data and storing historical data in the productionenvironment, wherein the real-time data is collected at a rate dependingon availability of the real-time data from a real-time data source;receiving at the query processor, a first query program with one or morefirst configuration instructions from the query client device; the queryprocessor determining from the one or more first configurationinstructions from the query client device that a real-time mode is to beused when executing the first query program; the query processorexecuting, in the real-time mode, the first query program in theproduction environment using production system code; receiving at thequery processor, a second query program with one or more secondconfiguration instructions from the query client device; the queryprocessor parsing the one or more second configuration instructions; thequery processor determining from the one or more second configurationinstructions from the query client device that a simulation mode is tobe used when executing the second query program; and requestingreal-time data simulated from the historical data stored in theproduction environment, wherein changes to the real-time data simulatedfrom the historical data made in preparation to use the altered data aremade after the historical data is read from a data store, and wherein asimulated results log captures logged events from a simulation run. 29.The method of claim 28, further comprising: requesting non-action systemgenerated historical data; and creating anti-look-ahead bias historicalfilters.
 30. The method of claim 29, further comprising: applying theanti-look-ahead bias historical data filters to the requested non-actionsystem generated real-time data simulated from historical data.
 31. Themethod of claim 28, further comprising: when a simulated real-timeaction system does not exist, constructing a simulated real-time actionsystem; the query processor connecting to the simulated real-time actionsystem; and generating simulated real-time action system data.
 32. Themethod of claim 28, further comprising: the query processor determiningfrom the parsing of the one or more second configuration instructions, asimulation clock cycle and a clock cycle speed; for each clock cycle,the query processor: starting a simulated clock cycle; determining anydata changes in the clock cycle; applying the data changes to an updatepropagation graph; and updating dynamic simulated real-time actionsystem dynamic tables.
 33. The method of claim 28, wherein real-timedata simulated from historical data includes one or more of sorting thedata by sequence ID or by time stamp.
 34. The method of claim 33,further comprising replacing the simulated real-time action system witha second simulated real-time action system.
 35. The method of claim 28,wherein the real-time data are simulated from historical data by readinga copy of underlying historical data and applying, after the reading,one or more changes to the read copy of underlying historical data; andwherein the underlying historical data are unchanged by the reading andthe applying.
 36. The method of claim 28, wherein the historical dataincludes data not available in a real-time environment.
 37. The methodof claim 28, further comprising: generating an update propagation graph(UPG) based on the second query program, the UPG having a plurality ofnodes each corresponding to one of a plurality of data objectsreferenced by the second query program, the UPG having a structurerepresenting the dependencies between the plurality of data objects inthe second query program; the query processor determining from theparsing of the one or more second configuration instructions asimulation clock cycle and a clock cycle speed; and for each clockcycle: starting a simulated clock cycle; determining data changes in theclock cycle; applying the data changes according to an order determinedby the UPG; and updating dynamic simulated real-time action systemdynamic tables.
 38. A method for using a computer system in a productionenvironment to execute query programs in a simulated mode, the methodcomprising: establishing a digital connection between a query processorand a query client device; the computer system receiving real-time dataand storing historical data in the production environment; receiving atthe query processor, a first query program with one or more firstconfiguration instructions from the query client device; the queryprocessor determining from the one or more first configurationinstructions from the query client device that a real-time mode is to beused when executing the first query program; the query processorexecuting, in the real-time mode, the first query program in theproduction environment using production system code; receiving at thequery processor, a second query program with one or more secondconfiguration instructions from the query client device; the queryprocessor parsing the one or more second configuration instructions; thequery processor determining from the one or more second configurationinstructions from the query client device that a simulation mode is tobe used when executing the second query program; the query processorextracting a simulation period from the one or more second configurationinstructions; the query processor beginning a simulated clock cycle; thequery processor loading real-time data simulated from the historicaldata corresponding to the simulated clock cycle; the query processormapping dynamic real-time data objects in existence during the simulatedclock cycle to the real-time simulated data; the query processorprocessing changes to the dynamic real-time data objects through one ormore update propagation graphs; the query processor updating the dynamicreal-time data objects based on the results from simulated actions; andif a next simulated clock cycle is within the simulated period,repeating the above until the simulation period ends.
 39. The method ofclaim 38, further comprising: the query processor determining a speed ofthe simulated clock cycle within the simulated period; and the queryprocessor passing results from simulated actions to simulated resultsand state logs.
 40. The method of claim 38, further comprising:requesting non-action system generated historical data; creatinganti-look-ahead bias historical filters; and applying theanti-look-ahead bias historical data filters to the requested non-actionsystem generated real-time data simulated from historical data.
 41. Themethod of claim 38, further comprising: when a simulated real-timeaction system does not exist, constructing a simulated real-time actionsystem; the query processor connecting to the simulated real-time actionsystem; generating simulated real-time action system data; and replacingthe simulated real-time action system with a second simulated real-timeaction system.
 42. The method of claim 38, wherein the historical dataincludes data not available in a real-time environment.
 43. The methodof claim 38, wherein the real-time data simulated from historical dataincludes one or more of sorting the real-time simulated data by sequenceID or by time stamp for only the simulated clock cycle prior to use. 44.The system of claim 21, wherein the one or more first configurationinstructions are different than the one or more second configurationinstructions.
 45. The system of claim 21, wherein the first queryprogram is different than the second query program.
 46. The method ofclaim 28, wherein the first query program is different than the secondquery program.
 47. The method of claim 28, wherein the one or more firstconfiguration instructions are different than the one or more secondconfiguration instructions.